January 15, 2024
C - Mathematical and Quantitative Methods
-
-
January 15, 2024
Flood risk and residential lending
We present key findings of a recent study that evaluates the credit risk that flooding poses to the residential lending activities of Canadian banks and credit unions. Results show that such risk currently appears modest but could become larger with climate change. -
Communicating Inflation Uncertainty and Household Expectations
We examine the value of direct communication to households about inflation and the uncertainty around inflation statistics. All types of information about inflation are effective at immediately managing inflation expectations, with information about outlooks being more effective and relevant than that about recent inflation and Bank targets. -
Predictive Density Combination Using a Tree-Based Synthesis Function
This paper studies non-parametric combinations of density forecasts. We introduce a regression tree-based approach that allows combination weights to vary on the features of the densities, time-trends or economic indicators. In two empirical applications, we show the benefits of this approach in terms of improved forecast accuracy and interpretability. -
Climate-Related Flood Risk to Residential Lending Portfolios in Canada
We assess the potential financial risks of current and projected flooding caused by extreme weather events in Canada. We focus on the residential real estate secured lending (RESL) portfolios of Canadian financial institutions (FIs) because RESL portfolios are an important component of FIs’ balance sheets and because the assets used to secure such loans are immobile and susceptible to climate-related extreme weather events. -
Understanding the Systemic Implications of Climate Transition Risk: Applying a Framework Using Canadian Financial System Data
Our study aims to gain insight on financial stability and climate transition risk. We develop a methodological framework that captures the direct effects of a stressful climate transition shock as well as the indirect—or systemic—implications of these direct effects. We apply this framework using data from the Canadian financial system. -
Finding the balance—measuring risks to inflation and to GDP growth
Using our new quantitative tool, we show how the risks to the inflation and growth outlooks have evolved over the course of 2023. -
Making It Real: Bringing Research Models into Central Bank Projections
Macroeconomic projections and risk analyses play an important role in guiding monetary policy decisions. Models are integral to this process. This paper discusses how the Bank of Canada brings research models and lessons learned from those models into the central bank projection environment. -
Testing Collusion and Cooperation in Binary Choice Games
This paper studies the testable implication of players’ collusive or cooperative behaviour in a binary choice game with complete information. I illustrate the implementation of this test by revisiting the entry game between Walmart and Kmart. -
Machine learning for economics research: when, what and how
This article reviews selected papers that use machine learning for economics research and policy analysis. Our review highlights when machine learning is used in economics, the commonly preferred models and how those models are used.